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Food sampling
Food sampling
Food sampling
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A food scientist considers canned peas

Food sampling is a process used to check that a food is safe and that it does not contain harmful contaminants, or that it contains only permitted additives at acceptable levels, or that it contains the right levels of key ingredients and its label declarations are correct, or to know the levels of nutrients present.

A food sample is carried out by subjecting the product to physical analysis. Analysis may be undertaken by or on behalf of a manufacturer regarding their own product, or for official food law enforcement or control purposes, or for research or public information.

To undertake any analysis, unless the whole amount of food to be considered is very small so that the food can be used for testing in its entirety, it is usually necessary for a portion of it to be taken (e.g. a small quantity from a full production batch, or a portion of what is on sale in a shop) – this process is known as food sampling.

In most cases with food to be analysed there are two levels of sampling – the first being selection of a portion from the whole, which is then submitted to a laboratory for testing, and the second being the laboratory's taking of the individual amounts necessary for individual tests that may be applied. It is the former that is 'food sampling': the latter is analytical laboratory 'sub-sampling', often relying upon initial homogenisation of the entire submitted sample.

Where it is intended that the results of any analysis to relate to the food as a whole it is crucially important that the sample is representative of that whole – and the results of any analysis can only be meaningful if the sampling is undertaken effectively. This is true whether the 'whole' is a manufacturer's entire production batch, or where it is a single item but too large to all be used for the test.

Factors relevant in considering the representativeness of a sample include the homogeneity of the food, the relative sizes of the sample to be taken and the whole, the potential degree of variation of the parameter(s) in question through the whole, and the significance and intended use of the analytical result.

Sampling by manufacturers

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Food manufacturers and producers would need to satisfy themselves that any sample taken for analysis is sufficiently representative of the food for the analytical result to be meaningful. This is true whether the data are to be used as the basis of labelling declarations, assurance of compliance with legislative or other standards, monitoring of production as part of HACCP (Hazard Analysis and Critical Control Points), or for routine quality control.

In the United Kingdom although various guidance is available, either from manufacturers' associations or from sources of standards such as British Standards Institution (such as British Standard BS6001), some of which may be relevant to certain food types. It is largely down to manufacturers to make their own evaluations of need and suitability. This must be translated into an assessment both of sample portion size and number, and the frequency of taking samples.

Testing and methods of analysis

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To ensure food safety and quality, some food samples which are perishable require certain tests and analyses. The following tests and analyses can be conducted:

Food law enforcement (UK)

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In the United Kingdom, enforcement is under the Food Safety Act 1990. Food sampling is undertaken primarily by local authorities and port health authorities for submission to public analysts for analysis. Much of the legislation relates to food as supplied to a consumer, meaning that every portion of a size of perishable food and foods at risk as may be supplied to a consumer has to comply, so that in such cases the sample submitted for analysis could simply be an entire consumer-sized portion. There are exceptions, however, such as the sampling of nut products for the presence of aflatoxins, which stipulate a primary sample size related to the size of the consignment – with associated requirements for initial homogenisation to produce a smaller sample to be sent for analysis.

The Food Safety Act 1990[1] affords a right for defence analysis, and for referee analysis in case of disputed analytical results, by stipulating that except where to do so would prevent effective analysis the sample must be divided into three parts. The UK Food Standards Agency[2] provides supplementary guidance to the enforcement authorities to assist with the sampling process and associated decisions by sampling officers.

There is no set frequency or rate for the sampling of food for law enforcement in the UK. Between the 1930s and 1990s there had been a guideline minimum rate for sampling for chemical analysis (not including samples for microbiological examination) of 2.5 samples per annum per 1000 head of population, however that was an arbitrary figure and more recent thinking suggested that the selection of a frequency for sampling should be based on risk. In this context risk includes all 'consumer protection' issues such as pecuniary disadvantage from substandard or counterfeit products, as well as risk to health. The Association of Public Analysts was commissioned by the Food Standards Agency to look into this, culminating in a scheme for Risk Based Sampling,[3][4] though it has not yet been adopted by the enforcement authorities.

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In the 1993 Seinfeld episode "The Non-Fat Yogurt", frozen yogurt is sent to a laboratory for food sampling, where it is found to, in fact, contain fat.[5]

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Food sampling

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from Grokipedia
Food sampling is the systematic process of collecting and testing representative portions of products, ingredients, environmental surfaces, or related materials to verify , detect contaminants, ensure , and confirm compliance with regulatory standards. This practice is essential in the to identify potential hazards such as biological pathogens, chemical residues, or physical impurities before they reach consumers, thereby preventing foodborne illnesses and protecting . The primary types of food sampling include product sampling, which involves analyzing finished or in-process s for contaminants and labeling accuracy; environmental sampling, which targets production facilities to uncover hidden sources of ; and emergency or targeted sampling, used to investigate outbreaks or emerging risks. Effective sampling relies on principles such as representativeness—ensuring the sample reflects the entire batch—along with aseptic collection techniques, proper labeling, and storage under controlled conditions to maintain sample integrity during transport and testing. In practice, food sampling supports broader food safety frameworks like the FDA's Food Safety Modernization Act and international guidelines from bodies such as Codex Alimentarius, enabling proactive monitoring of hazards in raw materials, processing lines, water supplies, and finished goods. By using accredited laboratories and standardized methods, sampling facilitates regulatory actions including recalls, import refusals, and process improvements, ultimately contributing to reduced contamination risks and enhanced consumer confidence in the global food supply.

Overview and Importance

Definition and Principles

Food sampling is the systematic process of selecting and extracting portions from a food lot or batch to represent the entire unit for evaluation of , , and compliance. This approach ensures that the chosen samples provide statistically valid inferences about the whole population, relying on probabilistic methods to achieve representativeness and account for variability within the batch. Central principles guiding food sampling include , which selects units without to mirror the distribution; stratification, which divides the lot into homogeneous subgroups (strata) based on key variations such as or production time before proportional sampling; and , which merges multiple subsamples into a single analytical unit to lower testing costs while preserving overall representation. evaluates whether a lot meets criteria by inspecting the sample and deciding to accept or reject the batch, while lot size determination scales the sample relative to batch volume, often using formulas like approximate sample size n ≈ 1 + √N (where N is lot size) to balance precision and efficiency. These principles originated in early 20th-century advancements in statistical , particularly Walter Shewhart's development of control charts in at Bell Laboratories, which introduced probabilistic monitoring of process variation and were subsequently applied to food production for defect detection and consistency. A key parameter in such plans is the (AQL), which represents the worst tolerable average quality level of a lot, typically expressed as a (e.g., 1% defective). This metric derives from the , modeling the number of defectives in a sample as a ; the criterion sets the probability of accepting a lot at the AQL to approximately 95%, computed via the P(X ≤ c | n, p) = \sum_{k=0}^{c} \binom{n}{k} p^k (1-p)^{n-k}, where n is sample size, p is the defective proportion (AQL/100), and c is the acceptance number.

Role in Food Safety and Quality Control

Food sampling plays a pivotal role in by enabling the detection of biological contaminants such as pathogens like , which can cause severe illnesses if ingested through contaminated products. It also identifies , including residues that may exceed safe limits, and physical hazards like foreign objects that pose choking or injury risks. These detection efforts are integral to and Critical Control Points (HACCP) systems, where sampling verifies that control measures effectively prevent, eliminate, or reduce hazards at critical production stages, thereby minimizing the risk of widespread contamination. In quality control, food sampling ensures consistency by monitoring key attributes such as nutritional content, which must align with labeling claims to meet regulatory standards and consumer expectations. It assesses through evaluations of stability under storage conditions, preventing premature spoilage that could compromise product integrity. Additionally, sampling evaluates sensory attributes like , texture, and appearance via organoleptic analysis, helping maintain product appeal and build consumer trust while reducing the incidence of recalls due to quality failures. The economic benefits of food sampling are substantial, as early defect detection through routine sampling avoids costly downstream issues, including product rework, disposal, and market withdrawals. By facilitating proactive interventions, it contributes to cost savings for producers and processors, potentially reducing treatment and expenses associated with events. Globally, foodborne illnesses affect an estimated 600 million people annually, leading to 420,000 deaths, and sampling protocols help mitigate these burdens by enabling timely identification and containment of hazards, thereby lowering associated healthcare and productivity losses. A notable case illustrating sampling's impact occurred during the 2018 multistate E. coli O157:H7 outbreak linked to romaine lettuce, where targeted sampling and traceback protocols by the FDA identified contaminated sources in the Yuma, Arizona, growing region, allowing for swift recalls and preventing further illnesses among the 210 reported cases. This event underscored how effective sampling integrates with regulatory responses to trace contamination pathways and protect public health.

Sampling Methods and Techniques

Types of Sampling Plans

Sampling plans in food quality control are broadly categorized into attribute sampling, variables sampling, and sequential sampling, each designed to assess lot based on specific criteria such as defect presence or measurable characteristics. Attribute sampling evaluates items as either conforming or nonconforming, often for pass/fail decisions on defects like microbial or foreign matter, using counts of defective units against predefined acceptance numbers. Variables sampling, in contrast, measures continuous quantities such as moisture content or levels, providing more precise data by analyzing statistical properties like means and variances to determine lot quality. Sequential sampling adapts dynamically, allowing inspectors to continue or halt testing based on accumulating results, which is particularly efficient for ongoing monitoring but less common in routine food microbiological assessments. Emerging approaches, such as Bayesian sampling plans, are gaining attention for incorporating prior knowledge into decision-making, as outlined in the amended informational document adopted at CCMAS44 in June 2025. Additionally, AI-assisted and hybrid sampling methods are being developed to optimize targeted sampling for risks. For detailed trends, see the Challenges and Innovations section. Standards-based plans, such as those outlined in MIL-STD-105E and its successor ANSI/ASQ Z1.4, provide tabulated procedures for attribute in industries including . These standards define single sampling schemes, where a fixed sample size n is inspected once and accepted if the number of defectives ≤ acceptance number c; double sampling, involving an initial smaller sample with a second if inconclusive; and multiple sampling, using several escalating sample sizes for finer discrimination. In food contexts, these are adapted for lot , with inspection levels scaled to batch sizes to balance risk and efficiency. Food-specific adaptations account for product perishability, influencing lot definitions and sample sizes to minimize degradation risks. For perishables like fresh produce, smaller lots and reduced sample sizes (e.g., n=5 for high-risk items) are preferred to ensure timely analysis, whereas non-perishables such as canned goods allow larger batches and bigger samples (e.g., n=13 or more) due to extended . The performance of these plans is evaluated using the operating characteristic (OC) curve, which plots the probability of acceptance P_a against the lot's proportion defective p. For attribute sampling, P_a is calculated as the cumulative binomial probability: Pa=∑k=0c(nk)pk(1−p)n−kP_a = \sum_{k=0}^{c} \binom{n}{k} p^k (1-p)^{n-k} where n is the sample size, c is the acceptance number, and (nk)\binom{n}{k} is the . This equation quantifies the plan's ability to discriminate between acceptable and rejectable lots, guiding selection for applications.

Collection and Handling Procedures

Collection and handling procedures in food sampling are critical to prevent , degradation, or alteration of samples, ensuring that subsequent analyses accurately reflect the original product's condition. These procedures begin with careful , where samplers identify representative portions of the lot or batch, often using stratified approaches for non-homogeneous materials to capture variability across the production area. Tools for collection vary by food type: corers or probes for solid foods like fruits and , sterile or syringes for liquids such as beverages or oils, and scoops or knives for semi-solids like products. Aseptic techniques are essential throughout, including wearing gloves, disinfecting equipment with 70% or autoclaving, and avoiding direct hand contact to minimize microbial introduction. Once collected, samples must follow strict handling protocols to maintain integrity. documentation tracks each transfer, recording details such as sampler's name, date, time, location, and condition of the sample, often using standardized forms to ensure legal admissibility and . is paramount: perishable items like meats and are refrigerated at 0-4°C immediately after collection, while frozen samples remain at -20°C or below using insulated containers with . Labeling standards require waterproof tags or adhesive labels with unique identifiers, including product description, collection site, and storage conditions, to prevent mix-ups during transport. For multi-phase products like emulsions or particulates in sauces, gentle mixing without excessive agitation preserves structure. Best practices are guided by international standards, such as the General Guidelines on Sampling (CAC/GL 50-2004), which emphasize using sterile, leak-proof containers and prompt delivery to laboratories within 24-36 hours for most foods; note that as of June 2025, CCMAS44 adopted an amended informational document expanding on sampling plans, including Bayesian methods. For heterogeneous foods like ground meats or composite products, multiple subsamples from different lot sections are combined and homogenized via blending at high speeds (e.g., 10,000 rpm for 2 minutes) to achieve uniformity without introducing heat that could affect analytes. The ISO/TS 17728:2015 provides specific techniques for microbiological sampling, recommending cooled sterile tools and avoiding cross-contact between samples. In contexts, ISO 707:2008 outlines tailored methods, such as using dippers for liquids and ensuring samples represent bulk volumes accurately. Common errors, such as cross-contamination, often arise from reusing non-sterilized equipment or inadequate separation of samples; mitigation involves dedicated tools per sample type and surface disinfection between collections. Improper temperature management can lead to microbial growth or chemical breakdown, underscoring the need for real-time monitoring devices during transport. Adhering to these protocols, informed by guidelines like the FDA's Bacteriological Analytical Manual, minimizes such risks and upholds sample reliability.

Laboratory Testing and Analysis

Common Analytical Methods

Common analytical methods in food sampling encompass microbiological, chemical, and physical techniques designed to detect pathogens, chemical contaminants, adulterants, and quality attributes in samples, ensuring and compliance with standards. These methods are applied after proper sample collection and handling to minimize or degradation. Microbiological analyses focus on viable microorganisms, while chemical and physical tests target molecular compositions and structural properties, respectively. Microbiological methods rely on culture-based plating as the foundational approach for enumerating in food samples. In the standard plate count procedure, serial dilutions of homogenized samples are spread onto plates, incubated under controlled conditions (typically 35–37°C for 24–48 hours), and viable cells are quantified as colony-forming units (CFU) per gram or milliliter, providing an estimate of total aerobic bacterial load. This technique, considered the gold standard due to its specificity for culturable organisms, is routinely used for routine monitoring in dairy, meat, and produce. For faster pathogen identification, (PCR) methods, particularly real-time PCR, amplify and detect DNA sequences specific to target organisms. In the case of Listeria monocytogenes, FDA-validated real-time PCR assays target the iap gene, enabling detection within 24–48 hours from enrichment broths, with sensitivities down to 1–10 CFU per sample after pre-enrichment. These molecular approaches complement traditional culturing by reducing time-to-result while maintaining high specificity. Chemical analyses employ chromatographic and spectroscopic techniques to identify and quantify residues and adulterants at trace levels. High-performance liquid chromatography (HPLC), frequently interfaced with tandem mass spectrometry (LC-MS/MS), is a primary method for pesticide residue analysis in fruits, vegetables, and grains. The AOAC Official Method 2007.01 outlines a QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) extraction protocol using acetonitrile and magnesium sulfate partitioning, followed by dispersive solid-phase extraction cleanup, achieving recoveries of 70–120% for over 200 pesticides at fortification levels of 10–100 µg/kg. Spectroscopy, such as Fourier transform infrared (FTIR), provides non-destructive screening for adulteration; for example, FTIR identifies melamine in milk by detecting its characteristic amide peaks at 1460–1550 cm⁻¹ and 3300–3500 cm⁻¹, with detection limits around 0.1–1% w/w in liquid milk samples. Physical tests evaluate macroscopic properties influencing consumer perception and . Rheometers perform texture analysis by applying controlled shear or compression forces to measure parameters like , elasticity modulus (G'), and yield stress, which indicate spreadability in sauces or chewiness in gels. These instruments use oscillatory or rotational modes to characterize non-Newtonian behaviors in complex food matrices, such as emulsions or doughs, correlating rheological data with sensory attributes. Moisture determination employs , an iodometric reaction where water reacts stoichiometrically with iodine in a medium containing and a base, quantified volumetrically or coulometrically to achieve accuracies of ±0.1–1 mg/kg in dry foods like cereals or powders. Validation of these methods ensures analytical performance, with the limit of detection (LOD) being a critical parameter for trace-level contaminants. The LOD represents the lowest concentration reliably distinguishable from and is calculated using the IUPAC-recommended : LOD=3σS\text{LOD} = \frac{3\sigma}{S} where σ\sigma is the standard deviation of responses from blank samples (reflecting ), and SS is the sensitivity or of the , derived from signal-to-noise ratios typically exceeding 3:1 for confident detection. In food applications, this metric guides method applicability, such as setting thresholds for residues below maximum residue limits.

Quality Assurance in Testing

Quality assurance in food sampling testing encompasses standardized protocols to validate the reliability, accuracy, and of analytical results, ensuring that laboratories maintain competence in detecting contaminants, nutrients, or other quality parameters in food matrices. Accreditation to ISO/IEC 17025 is a cornerstone of this process, requiring food testing laboratories to demonstrate technical competence through documented procedures for personnel training, equipment calibration, and method validation. This standard mandates regular proficiency testing, where laboratories analyze blind samples from external providers to benchmark performance against peers, and internal audits to identify and correct procedural deviations. For instance, proficiency testing in food analysis often involves inter-laboratory comparisons of pesticide residues or microbial counts to verify consistency across methods like or culture-based assays. To control variability, laboratories employ (CRMs), which are homogeneous, stable samples with certified concentrations traceable to international standards, used to validate method accuracy in matrices such as cereals or products. Blank samples, processed identically to test samples but without analytes, detect from reagents or equipment, while spike samples—test portions fortified with known amounts—assess recovery rates and method precision, typically targeting 80-120% recovery for reliable assessments. Inter-laboratory comparisons further enhance controls by distributing identical sample sets to multiple labs for , enabling statistical evaluation of agreement via z-scores to flag outliers and improve harmonization. Error management addresses sources like matrix effects, where complex food components such as fats or proteins interfere with detection in techniques like , leading to signal suppression or enhancement. Corrections often involve the method, in which incremental known amounts of are added to the food sample prior to analysis, allowing extrapolation of the true concentration from a plot that accounts for the matrix interference. Measurement uncertainty quantifies the potential variability in results, calculated per the Guide to the Expression of Uncertainty in Measurement (GUM), which combines Type A uncertainty (from statistical analysis of repeated measurements) and Type B uncertainty (from other sources like calibration or environmental factors). The combined standard uncertainty uu is given by: u=uType A2+uType B2u = \sqrt{u_{\text{Type A}}^2 + u_{\text{Type B}}^2}
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